Файл:Classification and regression trees Wires11.pdf

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Classification_and_regression_trees_Wires11.pdf(0 × 0 пикселей, размер файла: 475 КБ, MIME-тип: application/pdf)

Wei-Yin Loh

Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. C 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14–23 DOI: 10.1002/widm.8

The C source code for C4.5 may be obtained from http://www.rulequest.com/Personal/. RPART may be obtained from http://www.R-project.org. M5’ is part of the WEKA21 package at http://www.cs.waikato.ac.nz/ml/weka/. Software for CRUISE, GUIDE and QUEST may be obtained from http://www/stat.wisc.edu/∼loh/.


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